




import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase      :						Any							=  logging.get_logger(__name__)


_lowerCamelCase      :						Optional[int]							=  """https://openaipublic.azureedge.net/jukebox/models/"""
_lowerCamelCase      :						Optional[Any]							=  {
    """jukebox-1b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """1b_lyrics/prior_level_2.pth.tar""",
    ],
    """jukebox-5b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """5b_lyrics/prior_level_2.pth.tar""",
    ],
}







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			int:



       """simple docstring"""

       if key.endswith('''.model.1.bias'''		) and len(key.split('''.'''		)		) > 10:
              A__ 					=       key.replace('''.model.1.bias'''					,							'''.conv1d_1.bias'''		)
       elif key.endswith('''.model.1.weight'''		) and len(key.split('''.'''		)		) > 10:
              A__ 					=       key.replace('''.model.1.weight'''					,							'''.conv1d_1.weight'''		)
       elif key.endswith('''.model.3.bias'''		) and len(key.split('''.'''		)		) > 10:
              A__ 					=       key.replace('''.model.3.bias'''					,							'''.conv1d_2.bias'''		)
       elif key.endswith('''.model.3.weight'''		) and len(key.split('''.'''		)		) > 10:
              A__ 					=       key.replace('''.model.3.weight'''					,							'''.conv1d_2.weight'''		)

       if "conditioner_blocks.0." in key:
              A__ 					=       key.replace('''conditioner_blocks.0'''					,							'''conditioner_blocks'''		)

       if "prime_prior" in key:
              A__ 					=       key.replace('''prime_prior'''					,							'''encoder'''		)

       if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
              A__ 					=       key.replace('''.emb.'''					,							'''.'''		)

       if key.endswith('''k'''		):  # replace vqvae.X.k with vqvae.X.codebook
              return key.replace('''.k'''					,							'''.codebook'''		)
       if "y_emb." in key:
              return key.replace('''y_emb.'''					,							'''metadata_embedding.'''		)

       if "x_emb.emb." in key:
              A__ 					=       key.replace('''0.x_emb.emb'''					,							'''embed_tokens'''		)

       if "prime_state_ln" in key:
              return key.replace('''prime_state_ln'''					,							'''encoder.final_layer_norm'''		)
       if ".ln" in key:
              return key.replace('''.ln'''					,							'''.layer_norm'''		)
       if "_ln" in key:
              return key.replace('''_ln'''					,							'''_layer_norm'''		)

       if "prime_state_proj" in key:
              return key.replace('''prime_state_proj'''					,							'''encoder.proj_in'''		)
       if "prime_x_out" in key:
              return key.replace('''prime_x_out'''					,							'''encoder.lm_head'''		)
       if "prior.x_out" in key:
              return key.replace('''x_out'''					,							'''fc_proj_out'''		)
       if "x_emb" in key:
              return key.replace('''x_emb'''					,							'''embed_tokens'''		)

       return key







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		)  ->			int:



       """simple docstring"""

       A__ 					=       {}
       import re

       A__ 					=       re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''		)
       A__ 					=       re.compile(
           R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''		)
       A__ 					=       re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''		)

       A__ 					=       re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''		)
       A__ 					=       re.compile(
           R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''		)
       A__ 					=       re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''		)

       A__ 					=       re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''		)
       A__ 					=       re.compile(
           R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''		)
       A__ 					=       re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'''		)

       for original_key, value in state_dict.items():
              # rename vqvae.encoder keys
              if re_encoder_block_conv_in.fullmatch(lowercase_		):
                     A__ 					=       re_encoder_block_conv_in.match(lowercase_		)
                     A__ 					=       regex_match.groups()
                     A__ 					=       int(groups[2]		) * 2 + int(groups[3]		)
                     A__ 					=       f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
                     A__ 					=       re_encoder_block_conv_in.sub(lowercase_					,							lowercase_		)

              elif re_encoder_block_resnet.fullmatch(lowercase_		):
                     A__ 					=       re_encoder_block_resnet.match(lowercase_		)
                     A__ 					=       regex_match.groups()
                     A__ 					=       int(groups[2]		) * 2 + int(groups[3]		)
                     A__ 					=       {'''1''': 1, '''3''': 2}[groups[-2]]
                     A__ 					=       f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
                     A__ 					=       f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
                     A__ 					=       prefix + resnet_block
                     A__ 					=       re_encoder_block_resnet.sub(lowercase_					,							lowercase_		)

              elif re_encoder_block_proj_out.fullmatch(lowercase_		):
                     A__ 					=       re_encoder_block_proj_out.match(lowercase_		)
                     A__ 					=       regex_match.groups()
                     A__ 					=       f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
                     A__ 					=       re_encoder_block_proj_out.sub(lowercase_					,							lowercase_		)

              # rename vqvae.decoder keys
              elif re_decoder_block_conv_out.fullmatch(lowercase_		):
                     A__ 					=       re_decoder_block_conv_out.match(lowercase_		)
                     A__ 					=       regex_match.groups()
                     A__ 					=       int(groups[2]		) * 2 + int(groups[3]		) - 2
                     A__ 					=       f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
                     A__ 					=       re_decoder_block_conv_out.sub(lowercase_					,							lowercase_		)

              elif re_decoder_block_resnet.fullmatch(lowercase_		):
                     A__ 					=       re_decoder_block_resnet.match(lowercase_		)
                     A__ 					=       regex_match.groups()
                     A__ 					=       int(groups[2]		) * 2 + int(groups[3]		) - 2
                     A__ 					=       {'''1''': 1, '''3''': 2}[groups[-2]]
                     A__ 					=       f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
                     A__ 					=       f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
                     A__ 					=       prefix + resnet_block
                     A__ 					=       re_decoder_block_resnet.sub(lowercase_					,							lowercase_		)

              elif re_decoder_block_proj_in.fullmatch(lowercase_		):
                     A__ 					=       re_decoder_block_proj_in.match(lowercase_		)
                     A__ 					=       regex_match.groups()
                     A__ 					=       f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
                     A__ 					=       re_decoder_block_proj_in.sub(lowercase_					,							lowercase_		)

              # rename prior cond.model to upsampler.upsample_block and resnet
              elif re_prior_cond_conv_out.fullmatch(lowercase_		):
                     A__ 					=       re_prior_cond_conv_out.match(lowercase_		)
                     A__ 					=       regex_match.groups()
                     A__ 					=       int(groups[1]		) * 2 + int(groups[2]		) - 2
                     A__ 					=       f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
                     A__ 					=       re_prior_cond_conv_out.sub(lowercase_					,							lowercase_		)

              elif re_prior_cond_resnet.fullmatch(lowercase_		):
                     A__ 					=       re_prior_cond_resnet.match(lowercase_		)
                     A__ 					=       regex_match.groups()
                     A__ 					=       int(groups[1]		) * 2 + int(groups[2]		) - 2
                     A__ 					=       {'''1''': 1, '''3''': 2}[groups[-2]]
                     A__ 					=       f"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
                     A__ 					=       f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
                     A__ 					=       prefix + resnet_block
                     A__ 					=       re_prior_cond_resnet.sub(lowercase_					,							lowercase_		)

              elif re_prior_cond_proj_in.fullmatch(lowercase_		):
                     A__ 					=       re_prior_cond_proj_in.match(lowercase_		)
                     A__ 					=       regex_match.groups()
                     A__ 					=       f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
                     A__ 					=       re_prior_cond_proj_in.sub(lowercase_					,							lowercase_		)

              # keep original key
              else:
                     A__ 					=       original_key

              A__ 					=       replace_key(lowercase_		)

              if f"""{key_prefix}.{key}""" not in model_state_dict or key is None:
                     print(f"""failed converting {original_key} to {key}, does not match"""		)

              # handle missmatched shape
              elif value.shape != model_state_dict[f"""{key_prefix}.{key}"""].shape:
                     A__ 					=       model_state_dict[f"""{key_prefix}.{key}"""]
                     print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""		)
                     A__ 					=       original_key

              A__ 					=       original_key
              A__ 					=       value

       return new_dict







@torch.no_grad()
def SCREAMING_SNAKE_CASE							(			lowercase_=None					,							lowercase_=None		)  ->			str:



       """simple docstring"""

       for file in MODEL_MAPPING[model_name]:
              if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split("/"		)[-1]}"""		):
                     A__ 					=       requests.get(f"""{PREFIX}{file}"""					,							allow_redirects=lowercase_		)
                     os.makedirs(f"""{pytorch_dump_folder_path}/"""					,							exist_ok=lowercase_		)
                     open(f"""{pytorch_dump_folder_path}/{file.split("/"		)[-1]}"""					,							'''wb'''		).write(r.content		)

       A__ 					=       MODEL_MAPPING[model_name.split('''/'''		)[-1]]

       A__ 					=       JukeboxConfig.from_pretrained(lowercase_		)
       A__ 					=       JukeboxModel(lowercase_		)

       A__ 					=       []
       A__ 					=       {}
       for i, dict_name in enumerate(lowercase_		):
              A__ 					=       torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split("/"		)[-1]}"""		)['''model''']

              A__ 					=       {}
              for k in old_dic.keys():
                     if k.endswith('''.b'''		):
                            A__ 					=       old_dic[k]
                     elif k.endswith('''.w'''		):
                            A__ 					=       old_dic[k]
                     elif "level_2" not in dict_name and "cond.model." in k:
                            A__ 					=       old_dic[k]
                     else:
                            A__ 					=       old_dic[k]

              A__ 					=       '''vqvae''' if i == 0 else f"""priors.{3 - i}"""
              A__ 					=       fix_jukebox_keys(lowercase_					,							model.state_dict()					,							lowercase_					,							lowercase_		)
              weight_dict.append(lowercase_		)

       A__ 					=       weight_dict.pop(0		)
       model.vqvae.load_state_dict(lowercase_		)
       for i in range(len(lowercase_		)		):
              model.priors[i].load_state_dict(weight_dict[2 - i]		)

       Path(lowercase_		).mkdir(exist_ok=lowercase_		)
       with open(f"""{pytorch_dump_folder_path}/mapping.json"""					,							'''w'''		) as txtfile:
              json.dump(lowercase_					,							lowercase_		)

       print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""		)
       model.save_pretrained(lowercase_		)

       return weight_dict


if __name__ == "__main__":
  _lowerCamelCase      :						Any							=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""jukebox-5b-lyrics""",
      type=str,
      help="""Name of the model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default="""jukebox-5b-lyrics-converted""",
      type=str,
      help="""Path to the output PyTorch model directory.""",
  )
  _lowerCamelCase      :						Optional[int]							=  parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)










'''simple docstring'''





import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


_UpperCamelCase						:						str  =					datasets.utils.logging.get_logger(__name__)


@dataclass
class  snake_case__      (					datasets.BuilderConfig):
					a_        =					None
					a_        =					"utf-8"
					a_        =					None
					a_        =					None
					a_        =					True  # deprecated
					a_        =					None  # deprecated
					a_        =					10 << 20  # 10MB
					a_        =					None






class  snake_case__      (					datasets.ArrowBasedBuilder):
					a_        =					JsonConfig




					def 		A      (  self  :		Union[str, Any]      )    ->							Optional[int]:
								if self.config.block_size is not None:
											logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''      )
											UpperCAmelCase_      :       Union[str, Any]          =       self.config.block_size
								if self.config.use_threads is not True:
											logger.warning(
											    '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''      )
								if self.config.newlines_in_values is not None:
											raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''      )
								return datasets.DatasetInfo(features=self.config.features      )




					def 		A      (  self  :		Optional[int]				, _A  :		List[str]      )    ->							int:
								if not self.config.data_files:
											raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"      )
								UpperCAmelCase_      :       Optional[Any]          =       dl_manager.download_and_extract(self.config.data_files      )
								if isinstance(_A				, (str, list, tuple)      ):
											UpperCAmelCase_      :       List[Any]          =       data_files
											if isinstance(_A				, _A      ):
														UpperCAmelCase_      :       List[str]          =       [files]
											UpperCAmelCase_      :       List[str]          =       [dl_manager.iter_files(_A      ) for file in files]
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN				, gen_kwargs={'''files''': files}      )]
								UpperCAmelCase_      :       Tuple          =       []
								for split_name, files in data_files.items():
											if isinstance(_A				, _A      ):
														UpperCAmelCase_      :       Optional[Any]          =       [files]
											UpperCAmelCase_      :       List[Any]          =       [dl_manager.iter_files(_A      ) for file in files]
											splits.append(datasets.SplitGenerator(name=_A				, gen_kwargs={'''files''': files}      )      )
								return splits




					def 		A      (  self  :		List[Any]				, _A  :		pa.Table      )    ->							pa.Table:
								if self.config.features is not None:
											# adding missing columns
											for column_name in set(self.config.features      ) - set(pa_table.column_names      ):
														UpperCAmelCase_      :       Union[str, Any]          =       self.config.features.arrow_schema.field(_A      ).type
														UpperCAmelCase_      :       int          =       pa_table.append_column(_A				, pa.array([None] * len(_A      )				, type=_A      )      )
											# more expensive cast to support nested structures with keys in a different order
											# allows str <-> int/float or str to Audio for example
											UpperCAmelCase_      :       Dict          =       table_cast(_A				, self.config.features.arrow_schema      )
								return pa_table





					def 		A      (  self  :		Optional[Any]				, _A  :		List[Any]      )    ->							Optional[int]:
								for file_idx, file in enumerate(itertools.chain.from_iterable(_A      )      ):
											# If the file is one json object and if we need to look at the list of items in one specific field
											if self.config.field is not None:
														with open(_A				, encoding=self.config.encoding				, errors=self.config.encoding_errors      ) as f:
																	UpperCAmelCase_      :       Optional[int]          =       json.load(_A      )

														# We keep only the field we are interested in
														UpperCAmelCase_      :       Any          =       dataset[self.config.field]

														# We accept two format: a list of dicts or a dict of lists
														if isinstance(_A				, (list, tuple)      ):
																	UpperCAmelCase_      :       Optional[int]          =       set().union(*[row.keys() for row in dataset]      )
																	UpperCAmelCase_      :       str          =       {col: [row.get(_A      ) for row in dataset] for col in keys}
														else:
																	UpperCAmelCase_      :       Tuple          =       dataset
														UpperCAmelCase_      :       List[str]          =       pa.Table.from_pydict(_A      )
														yield file_idx, self._cast_table(_A      )

											# If the file has one json object per line
											else:
														with open(_A				, '''rb'''      ) as f:
																	UpperCAmelCase_      :       Tuple          =       0
																	# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																	# Set a default minimum value of 16kB if the chunk size is really small
																	UpperCAmelCase_      :       Optional[Any]          =       max(self.config.chunksize // 32				, 16 << 10      )
																	UpperCAmelCase_      :       Tuple          =       (
																	    self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
																	)
																	while True:
																				UpperCAmelCase_      :       Optional[Any]          =       f.read(self.config.chunksize      )
																				if not batch:
																							break
																				# Finish current line
																				try:
																							batch += f.readline()
																				except (AttributeError, io.UnsupportedOperation):
																							batch += readline(_A      )
																				# PyArrow only accepts utf-8 encoded bytes
																				if self.config.encoding != "utf-8":
																							UpperCAmelCase_      :       List[Any]          =       batch.decode(self.config.encoding				, errors=_A      ).encode('''utf-8'''      )
																				try:
																							while True:
																										try:
																													UpperCAmelCase_      :       int          =       paj.read_json(
																													    io.BytesIO(_A      )				, read_options=paj.ReadOptions(block_size=_A      )      )
																													break
																										except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																													if (
																													    isinstance(_A				, pa.ArrowInvalid      )
																													    and "straddling" not in str(_A      )
																													    or block_size > len(_A      )
																													):
																																raise
																													else:
																																# Increase the block size in case it was too small.
																																# The block size will be reset for the next file.
																																logger.debug(
																																    F"Batch of {len(_A      )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."      )
																																block_size *= 2
																				except pa.ArrowInvalid as e:
																							try:
																										with open(
																										    _A				, encoding=self.config.encoding				, errors=self.config.encoding_errors      ) as f:
																													UpperCAmelCase_      :       List[Any]          =       json.load(_A      )
																							except json.JSONDecodeError:
																										logger.error(F"Failed to read file '{file}' with error {type(_A      )}: {e}"      )
																										raise e
																							# If possible, parse the file as a list of json objects and exit the loop
																							if isinstance(_A				, _A      ):  # list is the only sequence type supported in JSON
																										try:
																													UpperCAmelCase_      :       Optional[int]          =       set().union(*[row.keys() for row in dataset]      )
																													UpperCAmelCase_      :       Any          =       {col: [row.get(_A      ) for row in dataset] for col in keys}
																													UpperCAmelCase_      :       Optional[int]          =       pa.Table.from_pydict(_A      )
																										except (pa.ArrowInvalid, AttributeError) as e:
																													logger.error(F"Failed to read file '{file}' with error {type(_A      )}: {e}"      )
																													raise ValueError(F"Not able to read records in the JSON file at {file}."      ) from None
																										yield file_idx, self._cast_table(_A      )
																										break
																							else:
																										logger.error(F"Failed to read file '{file}' with error {type(_A      )}: {e}"      )
																										raise ValueError(
																										    F"Not able to read records in the JSON file at {file}. "
																										    F"You should probably indicate the field of the JSON file containing your records. "
																										    F"This JSON file contain the following fields: {str(list(dataset.keys()      )      )}. "
																										    F"Select the correct one and provide it as `field='XXX'` to the dataset loading method. "      ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																				yield (file_idx, batch_idx), self._cast_table(_A      )
																				batch_idx += 1


